67 lines
2.0 KiB
Python
67 lines
2.0 KiB
Python
import gradio as gr
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from PIL import Image
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from transformers import BeitImageProcessor, BeitForImageClassification
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from PIL import Image
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def inference(img):
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pretrained_model_path = "beit-base-patch16-224-pt22k-ft22k"
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processor = BeitImageProcessor.from_pretrained(pretrained_model_path)
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model = BeitForImageClassification.from_pretrained(pretrained_model_path)
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inputs = processor(images=img, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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# model predicts one of the 21,841 ImageNet-22k classes
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predicted_class_idx = logits.argmax(-1).item()
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# print("Predicted class:", model.config.id2label[predicted_class_idx])
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return model.config.id2label[predicted_class_idx]
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title = "beit-base-patch16-224-pt22k-ft22k"
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description = "Gradio Demo for beit-base-patch16-224-pt22k-ft22k. To use it, simply upload your image, or click one of the examples to load them."
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article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>"
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examples=[['example_cat.jpg'],['Masahiro.png']]
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demo = gr.Interface(
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fn=inference,
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inputs=[gr.inputs.Image(type="pil")],
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outputs=gr.outputs.Textbox(),
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title=title,
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description=description,
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article=article,
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examples=examples)
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demo.launch()
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##
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# model_dir = "hub/animegan2-pytorch-main"
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# model_dir_weight = "hub/checkpoints/face_paint_512_v1.pt"
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#
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# model2 = torch.hub.load(
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# model_dir,
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# "generator",
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# pretrained=True,
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# progress=False,
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# source="local"
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# )
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# model1 = torch.load(model_dir_weight)
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# face2paint = torch.hub.load(
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# model_dir, 'face2paint',
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# size=512,side_by_side=False,
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# source="local"
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# )
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#
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# def inference(img, ver):
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# if ver == 'version 2 (🔺 robustness,🔻 stylization)':
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# out = face2paint(model2, img)
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# else:
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# out = face2paint(model1, img)
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# return out
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#
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